The Experts below are selected from a list of 525 Experts worldwide ranked by ideXlab platform
Joseph R Cavallaro - One of the best experts on this subject based on the ideXlab platform.
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Trellis-search based soft-input soft-output MIMO detector: Algorithm and VLSI architecture
IEEE Transactions on Signal Processing, 2012Co-Authors: Yang Sun, Joseph R CavallaroAbstract:In this paper, we propose a Trellis-search based soft-input soft-output detection algorithm and its very large scale integration (VLSI) architecture for iterative multiple-input multiple-output (MIMO) receivers. We construct a Trellis Diagram to represent the search space of a transmitted MIMO signal. With the Trellis model, we evenly distribute the workload of candidates searching among multiple Trellis nodes for parallel processing. The search complexity is significantly reduced because the number of candidates is greatly limited at each Trellis node. By leveraging the Trellis structure, we develop an approximate Log-MAP algorithm by using a small list of largest exponential terms to compute the LLR (log-likelihood ratio) values. The Trellis-search based detector has a fixed-complexity and is very suitable for parallel VLSI implementation. As a case study, we have designed and synthesized a Trellis-search based soft-input soft-output MIMO detector for a 4 × 4 16-QAM system using a 1.08 V TSMC 65 nm technology. The detector can achieve a maximum throughput of 1.7 Gb/s with a core area of 1.58 mm2.
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low complexity and high performance soft mimo detection based on distributed m algorithm through Trellis Diagram
International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Yang Sun, Joseph R CavallaroAbstract:This paper presents a novel low-complexity multiple-input multiple-output (MIMO) detection scheme using a distributed M-algorithm (DM) to achieve high performance soft MIMO detection. To reduce the searching complexity, we build a MIMO Trellis graph and split the searching operations among different nodes, where each node will apply the M-algorithm. Instead of keeping a global candidate list as the traditional detector does, this algorithm keeps multiple small candidate lists to generate soft information. Since the DM algorithm can achieve good BER performance with a small M, the sorting cost of the DM algorithm is lower than that of the conventional K-best MIMO algorithm. The proposed algorithm is very suitable for high speed parallel processing.
Xin Wang - One of the best experts on this subject based on the ideXlab platform.
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A New Trellis Model for MAC Layer Cooperative Retransmission Protocols
IEEE Transactions on Vehicular Technology, 2017Co-Authors: Wei Ni, Mehran Abolhasan, Brett Hagelstein, Xin WangAbstract:Comparison studies on timer-based distributed cooperative retransmission protocols are challenging, given a variety of backoff techniques. We propose a new unified model, which can characterize a wide range of cooperative retransmission protocols. The key idea is a new Trellis Diagram that extrapolates the retransmission probabilities in each timeslot to the entire cooperative process. Following the Trellis, performance metrics, such as success rate and collision intensity, can be derived in a structured manner. The new Trellis model, coupled with Markov techniques, can be also extended to analyze the distributed binary exponential backoff processes of cooperative retransmissions. Confirmed by simulations, the proposed Trellis model accurately reveals the impact of the relays' relative locations and density on different protocols. Our model also has the potential to be used as a management tool to adaptively configure protocol parameters.
R. Raheli - One of the best experts on this subject based on the ideXlab platform.
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On Trellis-based truncated-memory detection
IEEE Transactions on Communications, 2005Co-Authors: G Ferrari, Guilio Colavolpe, R. RaheliAbstract:We propose a general framework for Trellis-based detection over channels with infinite memory. A general truncation assumption enables the definition of a Trellis Diagram, which takes into account a considered portion of the channel memory and possible coding memory at the transmitter side. It is shown that Trellis-based maximum a posteriori (MAP) symbol detection algorithms, in the form of forward-backward (FB) algorithms, can be derived on the basis of this memory-truncation assumption. A general approach to the design of truncated-memory (TM) FB algorithms is proposed, and two main classes of algorithms, characterized by coupled and decoupled recursions, respectively, are presented. The complexity of the derived TM-FB algorithms is analyzed in detail. Moreover, it is shown that MAP sequence detection algorithms, based on the Viterbi algorithm, follow easily from one of the proposed classes. Looking backward at this duality between MAP symbol detection algorithms and MAP sequence detection algorithms, it is shown that previous solutions for one case can be systematically extended to the other case. The generality of the proposed framework is shown by considering various examples of stochastic channels. New detection algorithms, as well as generalizations of solutions previously published in the literature, are embedded in the proposed framework. The obtained results do suggest that the performance of the proposed detection algorithms ultimately depends on the truncation depth, almost regardless of the specific detection strategy.
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On recursive optimal detection of linear modulations in the presence of random fading
'Wiley', 1998Co-Authors: P. Castoldi, R. RaheliAbstract:In this paper, the problem of optimal detection of a linearly modulated digital signal transmitted over a fading channel is addressed. First, the issue of preliminary front-end processing in order to extract a sufficient statistics for information sequence detection from the continuous-time received signal is considered. Then, recursive algorithms are derived, which approximate optimal detection under the assumption of a linear and noisy channel modeled by a stochastic time-varying impulse response with arbitrary known statistics. Specific solutions are proposed for the Rayleigh fading channel typical of mobile radio communications. The information symbols are assumed to be grouped into blocks which are preceded and followed by known preamble and postamble, respectively This model encompasses both Time Division Multiple Access (TDMA) schemes and pilot symbol assisted transmission. Two detection schemes are considered and relevant recursive formulations proposed. More specifically, optimal detection in complete absence of Channel State Information (CSI) (blind detection) or in presence of perfect CSI at the beginning of the data block (trained detection) are investigated. It is shown that optimal detection performance may be practically attained by sampling the received signal with few samples per symbol interval and searching a Trellis Diagram of adequate size by means of a Viterbi algorithm. State-complexity reduction techniques based on Per-Survivor Processing (PSP) for both types of detection schemes are also considered
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Efficient Trellis search algorithms for adaptive MLSE on fast Rayleigh fading channels
'Institute of Electrical and Electronics Engineers (IEEE)', 1994Co-Authors: P. Castoldi, R. Raheli, G. MarinoAbstract:We analyze Trellis search algorithms for joint sequence estimation and channel tracking, assuming rapidly varying frequency-selective Rayleigh fading. The Generalized Viterbi Algorithm (GVA) and the M-Algorithm (MA) are considered for approximately searching the maximum likelihood path in the Trellis Diagram and compared with the Viterbi Algorithm (VA). All algorithms perform channel tracking utilizing Per-Survivor Processing (PSP) techniques by associating a channel estimate to each hypothetical Trellis path, according to the Least Mean Square (LMS) or Recursive Least Square (RLS) algorithm. To reproduce a typical mobile digital communication system, a Time-Division Multiple Access (TDMA) data frame is assumed, where each user transmits a block of information symbols with known preamble and tail. For Doppler bands up to one hundredth the symbol frequency and a three path delay profile, the MA is shown to exhibit a slightly better performance than the GVA for an equal number of survivors, and outperform the VA
G Ferrari - One of the best experts on this subject based on the ideXlab platform.
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On Trellis-based truncated-memory detection
IEEE Transactions on Communications, 2005Co-Authors: G Ferrari, Guilio Colavolpe, R. RaheliAbstract:We propose a general framework for Trellis-based detection over channels with infinite memory. A general truncation assumption enables the definition of a Trellis Diagram, which takes into account a considered portion of the channel memory and possible coding memory at the transmitter side. It is shown that Trellis-based maximum a posteriori (MAP) symbol detection algorithms, in the form of forward-backward (FB) algorithms, can be derived on the basis of this memory-truncation assumption. A general approach to the design of truncated-memory (TM) FB algorithms is proposed, and two main classes of algorithms, characterized by coupled and decoupled recursions, respectively, are presented. The complexity of the derived TM-FB algorithms is analyzed in detail. Moreover, it is shown that MAP sequence detection algorithms, based on the Viterbi algorithm, follow easily from one of the proposed classes. Looking backward at this duality between MAP symbol detection algorithms and MAP sequence detection algorithms, it is shown that previous solutions for one case can be systematically extended to the other case. The generality of the proposed framework is shown by considering various examples of stochastic channels. New detection algorithms, as well as generalizations of solutions previously published in the literature, are embedded in the proposed framework. The obtained results do suggest that the performance of the proposed detection algorithms ultimately depends on the truncation depth, almost regardless of the specific detection strategy.
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On Trellis-based truncatedmemory detection
2005Co-Authors: G Ferrari, Giulio Colavolpe, Associate Member, Riccardo RaheliAbstract:Abstract—We propose a general framework for Trellis-based detection over channels with infinite memory. A general truncation assumption enables the definition of a Trellis Diagram, which takes into account a considered portion of the channel memory and possible coding memory at the transmitter side. It is shown that Trellis-based maximum a posteriori (MAP) symbol detection algorithms, in the form of forward-backward (FB) algorithms, can be derived on the basis of this memory-truncation assumption. A general approach to the design of truncated-memory (TM) FB algorithms is proposed, and two main classes of algorithms, characterized by coupled and decoupled recursions, respectively, are presented. The complexity of the derived TM-FB algorithms is analyzed in detail. Moreover, it is shown that MAP sequence detection algorithms, based on the Viterbi algorithm, follow easily from one of the proposed classes. Looking backward at this duality between MAP symbol detection algorithms and MAP sequence detection algorithms, it is shown that previous solutions for one case can be systematically extended to the other case. The generality of the proposed framework is shown by considering various examples of stochastic channels. New detection algorithms, as well as generalizations of solutions previously published in the literature, are embedded in the proposed framework. The obtained results do suggest that the performance of the proposed detection algorithms ultimately depends on the truncation depth, almost regardless of the specific detection strategy. Index Terms—Forward-backward (FB) algorithm, iterative detection, maximum a posteriori (MAP) sequence/symbol detection, memory truncation, Trellis-based detection, Viterbi algorithm (VA). I
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On Trellis-based truncated-memory detection
2003Co-Authors: G Ferrari, Guilio Colavolpe, Riccardo RaheliAbstract:Abstract — In this paper we propose a general framework for detection over channels with infinite memory. A general truncation assumption leads automatically to the definition of a Trellis Diagram. A general approach to the design of forwardbackward (FB) algorithms is proposed and two main classes of FB algorithms (with coupled and decoupled recursions, respectively) are presented. Moreover, it is shown that sequence detection algorithms, in the form of a Viterbi algorithm (VA), follow easily from one of the proposed classes. The generality of the proposed framework is shown by applying it to a few stochastic channels. The performance of the proposed algorithms seem to ultimately depend on the truncation length, almost irrespectively of the specific detection strategy. I
Yang Sun - One of the best experts on this subject based on the ideXlab platform.
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Trellis-search based soft-input soft-output MIMO detector: Algorithm and VLSI architecture
IEEE Transactions on Signal Processing, 2012Co-Authors: Yang Sun, Joseph R CavallaroAbstract:In this paper, we propose a Trellis-search based soft-input soft-output detection algorithm and its very large scale integration (VLSI) architecture for iterative multiple-input multiple-output (MIMO) receivers. We construct a Trellis Diagram to represent the search space of a transmitted MIMO signal. With the Trellis model, we evenly distribute the workload of candidates searching among multiple Trellis nodes for parallel processing. The search complexity is significantly reduced because the number of candidates is greatly limited at each Trellis node. By leveraging the Trellis structure, we develop an approximate Log-MAP algorithm by using a small list of largest exponential terms to compute the LLR (log-likelihood ratio) values. The Trellis-search based detector has a fixed-complexity and is very suitable for parallel VLSI implementation. As a case study, we have designed and synthesized a Trellis-search based soft-input soft-output MIMO detector for a 4 × 4 16-QAM system using a 1.08 V TSMC 65 nm technology. The detector can achieve a maximum throughput of 1.7 Gb/s with a core area of 1.58 mm2.
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low complexity and high performance soft mimo detection based on distributed m algorithm through Trellis Diagram
International Conference on Acoustics Speech and Signal Processing, 2010Co-Authors: Yang Sun, Joseph R CavallaroAbstract:This paper presents a novel low-complexity multiple-input multiple-output (MIMO) detection scheme using a distributed M-algorithm (DM) to achieve high performance soft MIMO detection. To reduce the searching complexity, we build a MIMO Trellis graph and split the searching operations among different nodes, where each node will apply the M-algorithm. Instead of keeping a global candidate list as the traditional detector does, this algorithm keeps multiple small candidate lists to generate soft information. Since the DM algorithm can achieve good BER performance with a small M, the sorting cost of the DM algorithm is lower than that of the conventional K-best MIMO algorithm. The proposed algorithm is very suitable for high speed parallel processing.
